Pre-Analysis Sample Preparation

After the identification of subpar performance sequences, the sample was prepared in the following steps. First, as Mata and Portugal (2000: 555) note, large datasets like the Toyo Keizai dataset, while very valuable in terms of explanatory power, may contain a higher absolute number of coding errors than hand-picked small datasets. To alleviate this concern of coding errors as best as possible, we scrutinized all the variables in the analysis. A variable that required adjustment was subsidiary age. Subsidiary age was calculated by subtracting the year of foundation from each year of observation. Subsidiaries that had a negative age value were deleted since this suggested a coding/input error in the year of foundation variable. In total, however, only about 0.18 percent of the dataset were affected, leading us to be confident in deleting these subsidiaries without affecting any analysis outcomes.

Second, since this study is only concerned with subsidiaries that are experiencing subpar performance sequences, the sample was cut to only include those sequences. Some subsidiaries may experience a number of such sequences, interrupted by periods of better performance or non-observance. Thus, there are likely going to be gaps (i.e. intervals) between the sequences of subpar performance, if the subsidiary experiences more than one such sequence. Following Cleves, Gould, Gutierrez, and Marchenko (2008: 36), the observations during such gaps were omitted. The same was done with observations that occurred before the first subpar performance sequence (left censoring). Moreover, as will be described in section 5.8, the fact that subsidiaries may encounter more than one subpar performance sequence was accounted for by creating robust standard errors through clustering the analysis by each subsidiary.

Third, Inkpen and Beamish (1998: 38) recommended excluding subsidiaries from the sample which contain fewer than 20 employees. This approach is a now common method to

ensure generalizability to substantive operations, not merely agencies or sales offices. Since the unit of analysis in this thesis is the subpar performance sequence, however, the application of this criterion was not as straightforward as merely deleting these respective observations. For instance, following the simple deletion method, a subsidiary that reported 40 employees at the beginning of the subpar performance sequence and then retrenched to 18 employees would have been included with an incomplete sequence. Similarly, a subsidiary which first reduced its workforce to fewer than 20 employees during the downturn phase and increased it again to more than 20 employees during the upturn phase would have been included into the sample with a holey sequence. Therefore, in an effort to include as many sequences with as much complete and continuous information as possible, we excluded only those sequences where the subsidiary reported fewer than 20 employees for the entire duration of the subpar performance sequence.

Fourth, since the objective of this thesis is to assess responses to subpar performance when such subpar performance does not occur by chance or due to short-term fluctuations, we omitted the first two years of each sequence (unless otherwise specified). As described in Chapter 2, this approach is in line with several decline/turnaround scholars, such as Tangpong et al., (2015).

These steps led to final pre-analysis sample sizes and characteristics as illustrated per performance measure in Table 5.1. As Table 5.1 shows, some subsidiaries may experience more than one subpar performance sequence, indicated by the higher number of sequences than subsidiaries. Moreover, given that labor productivity is a ratio of sales over the number of employees, it may seem surprising that the number of observations is higher than for the sales measure of performance. Upon closer inspection, however, the difference occurs when the level of sales does not change but the number of employees does, thereby leading to a higher

probability of being flagged as experiencing subpar performance compared to considering sales only.

Table 5.1. Sample Sizes per Performance Measure.

Performance measure Number of observa- tions Number of subsidiaries Number of sequences Max length Mean length S.D. length Number of countries Sales 17,982 5,669 7,406 18 4.41 1.84 94 Labor productivity 21,860 6,307 8,744 22 4.45 1.85 87 Perceptual measures of financial performance A: (0=surplus, 1=break-even, deficit) B: (0=surplus/break- even, 1=deficit) 11,847 4,633 3,196 1,553 3,360 1,592 24 14 5.24 4.75 2.38 1.95 73 56 Note: Observations are subsidiary-year occurrences. Length refers to subpar performance sequences.

In this thesis, the main operationalization of subpar performance sequences was based on the sales differentials measure of performance. It was selected for three reasons. First, as Weinzimmer, Nystrom, and Freeman (1998: 235) note, sales growth is the “most commonly identified measure of overall organizational performance (Hubbard & Bromiley, 1995)” and any decline in sales may thus indicate a decline in subsidiary growth. Moreover, sales may be a more fitting measure than increases in employees or assets since “a firm can realize growth in sales dollars without achieving any significant change in employees or assets” and thus, “sales data may be more appropriate in studies including organizations” from different industries (Weinzimmer et al., 1998: 252). Second, with a labor productivity measure, decreases in the sales-vs-employees ratio may occur due to the hiring of more employees, with there being a time lag until sales growth has caught up with the increased number of employees. Thus, a common approach to growth by investing in human resources may be flagged as an indication of decline. This can be especially salient in service subsidiaries which tend to be more labor-intensive. A

labor productivity measure may therefore be a more noisy measure of decline than using sales differentials as the measure of performance, especially when including both manufacturing and service subsidiaries into one sample (Weinzimmer et al., 1998: 252). Third, compared to perceptual measures of financial performance, focusing on sales differentials offers a larger sample size and reduces the risk of biases such as retrospective bias or social desirability bias. Nonetheless, the other types of performance will be used as robustness checks in Chapter 6.